System and methods for improving patent registration

ABSTRACT

The application as disclosed herein describes systems and methods for performing patient registration using a surgical navigation system. This application attempts to provide for improvements upon existing systems through the addition of refinement steps used to increase the accuracy of registration and streamline said process. In some cases the improvements generally involve the manipulation of spatial data in an iterative manner.

TECHNICAL FIELD

The present disclosure is generally related to neurosurgical or medicalprocedures, and more specifically to methods for improving the surfacetrace patient registration process using a medical navigation system.

BACKGROUND

In the field of medicine, imaging and image guidance are a significantcomponent of clinical care. From diagnosis and monitoring of disease, toplanning of the surgical approach, to guidance during procedures andfollow-up after the procedure is complete, imaging and image guidanceprovides effective and multifaceted treatment approaches, for a varietyof procedures, including surgery and radiation therapy. Targeted stemcell delivery, adaptive chemotherapy regimes, and radiation therapy areonly a few examples of procedures utilizing imaging guidance in themedical field.

Advanced imaging modalities such as Magnetic Resonance Imaging (“MRI”)have led to improved rates and accuracy of detection, diagnosis andstaging in several fields of medicine including neurology, where imagingof diseases such as brain cancer, stroke, Intra-Cerebral Hemorrhage(“ICH”), and neurodegenerative diseases, such as Parkinson's andAlzheimer's, are performed. As an imaging modality, MRI enablesthree-dimensional visualization of tissue with high contrast in softtissue without the use of ionizing radiation. This modality is oftenused in conjunction with other modalities such as Ultrasound (“US”),Positron Emission Tomography (“PET”) and Computed X-ray Tomography(“CT”), by examining the same tissue using the different physicalprincipals available with each modality. CT is often used to visualizeboney structures and blood vessels when used in conjunction with anintra-venous agent such as an iodinated contrast agent. MRI may also beperformed using a similar contrast agent, such as an intra-venousgadolinium based contrast agent which has pharmaco-kinetic propertiesthat enable visualization of tumors and break-down of the blood brainbarrier. These multi-modality solutions can provide varying degrees ofcontrast between different tissue types, tissue function, and diseasestates. Imaging modalities can be used in isolation, or in combinationto better differentiate and diagnose disease.

In neurosurgery, for example, brain tumors are typically excised throughan open craniotomy approach guided by imaging. The data collected inthese solutions typically consists of CT scans with an associatedcontrast agent, such as iodinated contrast agent, as well as MRI scanswith an associated contrast agent, such as gadolinium contrast agent.Also, optical imaging is often used in the form of a microscope todifferentiate the boundaries of the tumor from healthy tissue, known asthe peripheral zone. Tracking of instruments relative to the patient andthe associated imaging data is also often achieved by way of externalhardware systems such as mechanical arms, or radiofrequency or opticaltracking devices. As a set, these devices are commonly referred to assurgical navigation systems.

During a medical procedure, navigation systems require a registrationprocess to transform between the physical position of the patient in theoperating room and the volumetric image set (e.g., MRI/CT) being used asa reference to assist in accessing the target area in the patient.Conventionally, this registration is done relative to the position of apatient reference, which is visible by the tracking system and staysfixed in position and orientation relative to the patient throughout theprocedure.

This registration is typically accomplished through a touch-pointregistration method which involves constructing a correspondence ofidentifiable points (e.g., either fiducial or anatomic points) betweenthe patient in the operating room and the volumetric image set of thepatient. Such an approach to registration has a number of disadvantages,such as those that increase effort on the parts of the surgical teamincluding requiring fiducials to be placed before patient scans,requiring points to be identified one at a time, requiring points to bereacquired. Additionally disadvantages of this method also affect theaccuracy of the guidance system, such as providing for a limited numberof points, touch point collection is subject to user variability, andthe physical stylus used for collecting the points can deform or deflectpatient skin position, in addition the patient is required to be imageddirectly before the procedure and the fiducials may move/fall off.

Another approach to performing a registration is the surface traceregistration method which involves acquiring a contour of the patient,by drawing a line over the surface of the patient, usually acquiring aseries of points, using either a tracked stylus pointer or a laserpointer and fitting that contour to the corresponding extracted surfacefrom an image of the patient.

SUMMARY

The following application generally discloses a computer implementedmethod for performing a patient registration using a processor of asurgical navigation system in a medical procedure, comprising the stepsof initializing a surface trace acquisition, recording one or moresurface traces, terminating the surface trace acquisition, receiving apatient image of a patient anatomy, extracting a surface from thepatient image, and computing a registration transform for patientregistration between the one or more surface traces and the patientimage extracted surface. This method may also comprise computing theregistration transform by minimizing a set of Euclidean distances. Insome embodiments the step of computing a registration transform maycomprise iteratively inputting registration transforms into a costminimization function. In other embodiments the set of Euclideandistances used to compute the patient registration transform may includeat least the distances between the surface traces and the extractedsurface. In addition the method may comprise the steps of: initializinga fiducial position acquisition, recording the positions of fiducials onthe patient, and receiving the location of fiducials points in thepatient image. In other instances the set of Euclidean distances mayinclude at least the distances between the surface traces and theextracted surface and the distances between the fiducials and thefiducial points. In yet further embodiments the method may include thesteps of: monitoring the position of a pointer tool, analyzing theposition to determine if the pointer tool is motionless, and upondetermining that the pointer tool is motionless for a predeterminedamount of time prompting the surgical navigation system to initialize orterminate the surface trace. Furthermore the method may also comprisethe steps of: receiving input from a user ranking the one or moresurface traces, computing a weighting for the surface traces based onthe ranking, applying the weighting to the surface traces, and computingthe registration transform that minimizes a set of Euclidean distancesbetween the one or more surface traces and the surface. In yet furtherembodiments the methods may further comprise: receiving input from auser of one or more regions of one or more surface traces to be culled,discarding the one or more regions from the one or more surface traces,and computing a registration transform that minimizes a set of Euclideandistances between the one or more surface traces and the surface afterthe regions have been discarded. In some instances the method may alsocomprise the steps of: segmenting the patient image into regions,determining the spatial distribution of surface traces amongst theregions, determining whether the spatial distribution in each regionminimize deviance below a threshold, and upon determining the spatialdistribution in a region is above the threshold informing the user ofthe regions. It should be noted that the step of segmenting the patientimage into regions, may further entail doing so such that each regioncontains an anatomical landmark such as the naison, the temples, theears, the tip of the nose, the bridge of the nose, the shelves over theeyes, and etc. In some alternate instances the method may also comprise:initializing one or more landmark acquisitions, recording the positionsof one or more landmarks on a patient, receiving the position of one ormore landmark points in the patient image, and computing an initialregistration transform that minimizes a set of Euclidean distancesbetween the one or more landmarks and the one or more landmark points.The method as disclosed herein may also comprise using the initialregistration transform to visualize an initial alignment of thepatient's position with the patient image in an image space as well asvisualizing the surface traces in the image space and this resultantlymay assist the user in acquiring the surface traces.

Also generally disclosed in this application is a surgical navigationsystem used for navigated surgical procedures generally comprising: atracked pointer tool for identifying positions on the patient, atracking system for tracking the pointer tool, and a processorprogrammed with instruction to: initialize a surface trace acquisition,continuously record the positions of the pointer tool during thesurface; trace acquisition, combine the positions recorded during thesurface trace acquisition into a surface trace, terminate the surfacetrace acquisition, receive a patient image of the patient, extract asurface from the patient image, and compute a registration transformbetween the one or more surface traces and the surface for patientregistration. It should be noted that this system may also compute aregistration transform wherein this computation includes minimizing aset of Euclidean distances. In some instances the computation of aregistration transform may further comprise iteratively inputtingregistration transforms into a cost minimization function. In yet otherinstances the set of Euclidean distances may include at least thedistances between the surface traces and the surface. In someembodiments the processor may be programmed with further instructionscomprising: initialize a fiducial position acquisition, record theposition of the pointer tool during the fiducial position acquisition,and receive the location of fiducials points in the patient image. Inalternate embodiments the set of Euclidean distances may include atleast the distances between the surface traces and the surface and thedistances between the fiducial positions and the fiducial points. Instill yet alternate embodiments the processor may be programmed withfurther instructions comprising: monitor the position of the pointertool with the tracking system by recording the pointer tool positions,analyze the pointer tool positions to determine if the pointer tool ismotionless, and upon determining that the pointer tool is motionless fora predetermined amount of time prompting the processor to initialize thesurface trace acquisition. Furthermore the processor may be programmedwith further instructions comprising: receiving input from a userranking the one or more surface traces, computing a weighting for thesurface traces based on the ranking, applying the weighting to thesurface traces, and computing a registration transform that minimizes aset of Euclidean distances between the one or more surface traces andthe surface. Again the processor may in some instances be programmedwith further instructions comprising: receiving input from a user of oneor more regions of one or more surface traces to be culled, discardingthe one or more regions from the one or more surface traces, andcomputing a registration transform that minimizes a set of Euclideandistances between the one or more surface traces and the surface afterthe regions have been discarded. The system as described herein my insome instances also comprise a display having a GUI for receiving inputfrom a user, while the processor may be programmed with furtherinstructions to: initializing one or more landmark acquisitions,recording the positions of one or more landmarks on a patient, receivingthe position of one or more landmark points in the patient image; andcomputing an initial registration transform that minimizes a set ofEuclidean distances between the one or more landmarks and the one ormore landmark points. In yet further embodiments the processor may beprogrammed with further instructions comprising: using the initialregistration transform to visualize, on the display, an initialalignment of the patient's position with the patient image in an imagespace and to visualize the surface traces on the display.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments will now be described, by way of example only, withreference to the drawings, in which:

FIG. 1 illustrates the insertion of an access port into a human brain,for providing access to internal brain tissue during a medicalprocedure;

FIG. 2 shows an exemplary navigation system to support minimallyinvasive access port-based surgery;

FIG. 3 is a block diagram illustrating a control and processing systemthat may be used in the navigation system shown in FIG. 2;

FIG. 4A is a flow chart illustrating a method involved in a surgicalprocedure using the navigation system of FIG. 2;

FIG. 4B is a flow chart illustrating a method of registering a patientfor a surgical procedure as outlined in FIG. 4A;

FIG. 5 illustrates an explanatory diagram regarding the coupling of twocoordinate spaces;

FIG. 6 is a flow chart illustrating two methods of registering a patientfor a medical procedure with a medical navigation system;

FIG. 7 is a diagram depicting one of the methods in FIG. 6;

FIG. 8 is a diagram depicting a transform by method 621 in FIG. 6;

FIG. 9 is a diagram depicting method 601 of the methods in FIG. 6;

FIG. 10 is a diagram depicting a transform by method 601 in FIG. 6;

FIG. 11 illustrates three flow charts describing further enhancements tomethod 601 in FIG. 6;

FIG. 12 is a diagram depicting the effect of the first enhancement inFIG. 11;

FIG. 13 is a diagram depicting the effect of the second enhancement inFIG. 11;

FIG. 14 is a diagram depicting the effect of the third enhancement inFIG. 11;

FIG. 15 is a diagram depicting a display showing a number of surfacetraces acquired after an initial alignment is provided for patientregistration.

DETAILED DESCRIPTION

Various embodiments and aspects of the disclosure will be described withreference to details discussed below. The following description anddrawings are illustrative of the disclosure and are not to be construedas limiting the disclosure. Numerous specific details are described toprovide a thorough understanding of various embodiments of the presentdisclosure. However, in certain instances, well-known or conventionaldetails are not described in order to provide a concise discussion ofembodiments of the present disclosure.

As used herein, the terms, “comprises” and “comprising” are to beconstrued as being inclusive and open ended, and not exclusive.Specifically, when used in the specification and claims, the terms,“comprises” and “comprising” and variations thereof mean the specifiedfeatures, steps or components are included. These terms are not to beinterpreted to exclude the presence of other features, steps orcomponents.

As used herein, the term “exemplary” means “serving as an example,instance, or illustration,” and should not be construed as preferred oradvantageous over other configurations disclosed herein.

As used herein, the terms “about”, “approximately”, and “substantially”are meant to cover variations that may exist in the upper and lowerlimits of the ranges of values, such as variations in properties,parameters, and dimensions. In one non-limiting example, the terms“about”, “approximately”, and “substantially” mean plus or minus 10percent or less.

Unless defined otherwise, all technical and scientific terms used hereinare intended to have the same meaning as commonly understood by one ofordinary skill in the art. Unless otherwise indicated, such as throughcontext, as used herein, the following terms are intended to have thefollowing meanings:

As used herein, the phrase “access port” refers to a cannula, conduit,sheath, port, tube, or other structure that is insertable into asubject, in order to provide access to internal tissue, organs, or otherbiological substances. In some embodiments, an access port may directlyexpose internal tissue, for example, via an opening or aperture at adistal end thereof, and/or via an opening or aperture at an intermediatelocation along a length thereof. In other embodiments, an access portmay provide indirect access, via one or more surfaces that aretransparent, or partially transparent, to one or more forms of energy orradiation, such as, but not limited to, electromagnetic waves andacoustic waves.

As used herein the phrase “intraoperative” refers to an action, process,method, event or step that occurs or is carried out during at least aportion of a medical procedure. Intraoperative, as defined herein, isnot limited to surgical procedures, and may refer to other types ofmedical procedures, such as diagnostic and therapeutic procedures.

Embodiments of the present disclosure provide imaging devices that areinsertable into a subject or patient for imaging internal tissues, andmethods of use thereof. Some embodiments of the present disclosurerelate to minimally invasive medical procedures that are performed viaan access port, whereby surgery, diagnostic imaging, therapy, or othermedical procedures (e.g. minimally invasive medical procedures) areperformed based on access to internal tissue through the access port.

The present disclosure is generally related to medical procedures,neurosurgery, and patient registration to be specific.

In the example of a port-based surgery, a surgeon or robotic surgicalsystem may perform a surgical procedure involving tumor resection inwhich the residual tumor remaining after is minimized, while alsominimizing the trauma to the healthy white and grey matter of the brain.In such procedures, trauma may occur, for example, due to contact withthe access port, stress to the brain matter, unintentional impact withsurgical devices, and/or accidental resection of healthy tissue. A keyto minimizing trauma is ensuring that the spatial location of thepatient as understood by the surgeon and the surgical system is asaccurate as possible.

FIG. 1 illustrates the insertion of an access port into a human brain,for providing access to internal brain tissue during a medicalprocedure. In FIG. 1, access port 12 is inserted into a human brain 10,providing access to internal brain tissue. Access port 12 may includeinstruments such as catheters, surgical probes, or cylindrical portssuch as the NICO Brain Path. Surgical tools and instruments may then beinserted within the lumen of the access port in order to performsurgical, diagnostic or therapeutic procedures, such as resecting tumorsas necessary. The present disclosure applies equally well to catheters,DBS needles, a biopsy procedure, and also to biopsies and/or cathetersin other medical procedures performed on other parts of the body wherehead immobilization is needed.

In the example of a port-based surgery, a straight or linear access port12 is typically guided down a sulci path of the brain. Surgicalinstruments would then be inserted down the access port 12.

Optical tracking systems, which may be used in the medical procedure,track the position of a part of the instrument that is withinline-of-site of the optical tracking camera. In some embodiments theseoptical tracking systems also require a reference to the patient to knowwhere the instrument is relative to the target (e.g., a tumor) of themedical procedure. These optical tracking systems require a knowledge ofthe dimensions of the instrument being tracked so that, for example, theoptical tracking system knows the position in space of a tip of amedical instrument relative to the tracking markers being tracked.

Referring to FIG. 2, an exemplary navigation system environment 200 isshown, which may be used to support navigated image-guided surgery. Asshown in FIG. 2, surgeon 201 conducts a surgery on a patient 202 in anoperating room (OR) environment. A medical navigation system 205comprising an equipment tower, tracking system, displays and trackedinstruments assist the. 201 during his procedure. An operator 203 isalso present to operate, control and provide assistance for the medicalnavigation system 205.

Referring to FIG. 3, a block diagram is shown illustrating a control andprocessing system 300 that may be used in the medical navigation system200 shown in FIG. 2 (e.g., as part of the equipment tower). As shown inFIG. 3, in one example, control and processing system 300 may includeone or more processors 302, a memory 304, a system bus 306, one or moreinput/output interfaces 308, a communications interface 310, and storagedevice 312. Control and processing system 300 may be interfaced withother external devices, such as tracking system 321, data storage 342,and external user input and output devices 344, which may include, forexample, one or more of a display, keyboard, mouse, sensors attached tomedical equipment, foot pedal, and microphone and speaker. Data storage342 may be any suitable data storage device, such as a local or remotecomputing device (e.g. a computer, hard drive, digital media device, orserver) having a database stored thereon. In the example shown in FIG.3, data storage device 342 includes identification data 350 foridentifying one or more medical instruments 360 and configuration data352 that associates customized configuration parameters with one or moremedical instruments 360. Data storage device 342 may also includepreoperative image data 354 and/or medical procedure planning data 356.Although data storage device 342 is shown as a single device in FIG. 3,it will be understood that in other embodiments, data storage device 342may be provided as multiple storage devices.

Medical instruments 360 are identifiable by control and processing unit300. Medical instruments 360 may be connected to and controlled bycontrol and processing unit 300, or medical instruments 360 may beoperated or otherwise employed independent of control and processingunit 300. Tracking system 321 may be employed to track one or more ofmedical instruments 360 and spatially register the one or more trackedmedical instruments to an intraoperative reference frame. For example,medical instruments 360 may include tracking markers such as trackingspheres that may be recognizable by a tracking camera 307. In oneexample, the tracking camera 307 may be an infrared (IR) trackingcamera. In another example, as sheath placed over a medical instrument360 may be connected to and controlled by control and processing unit300.

Control and processing unit 300 may also interface with a number ofconfigurable devices, and may intraoperatively reconfigure one or moreof such devices based on configuration parameters obtained fromconfiguration data 352. Examples of devices 320, as shown in FIG. 3,include one or more external imaging devices 322, one or moreillumination devices 324, a robotic arm 305, one or more projectiondevices 328, a 3D imager 309, and one or more displays 311. It should benoted that the 3D imager may include devices such as a preoperative orintraopertive CT, MRI, Ultrasound, OCT, or Structured light imagingprobes and the like.

Exemplary aspects of the disclosure may be implemented via processor(s)302 and/or memory 304. For example, the functionalities described hereincan be partially implemented via hardware logic in processor 302 andpartially using the instructions stored in memory 304, as one or moreprocessing modules or engines 370. Example processing modules include,but are not limited to, user interface engine 372, tracking module 374,motor controller 376, image processing engine 378, image registrationengine 380, procedure planning engine 382, navigation engine 384, andcontext analysis module 386. While the example processing modules areshown separately in FIG. 3, in one example the processing modules 370may be stored in the memory 304 and the processing modules may becollectively referred to as processing modules 370.

It is to be understood that the system is not intended to be limited tothe components shown in FIG. 3. One or more components of the controland processing system 300 may be provided as an external component ordevice. In one example, navigation module 384 may be provided as anexternal navigation system that is integrated with control andprocessing system 300.

Some embodiments may be implemented using processor 302 withoutadditional instructions stored in memory 304. Some embodiments may beimplemented using the instructions stored in memory 304 for execution byone or more general purpose microprocessors. Thus, the disclosure is notlimited to a specific configuration of hardware and/or software.

While some embodiments can be implemented in fully functioning computersand computer systems, various embodiments are capable of beingdistributed as a computing product in a variety of forms and are capableof being applied regardless of the particular type of machine orcomputer readable media used to actually effect the distribution.

According to one aspect of the present application, one purpose of thenavigation system 205, which may include control and processing unit300, is to provide tools to the neurosurgeon that will lead to the mostinformed, least damaging neurosurgical operations. In addition toremoval of brain tumors and intracranial hemorrhages (ICH), thenavigation system 205 can also be applied to a brain biopsy, afunctional/deep-brain stimulation, a catheter/shunt placement procedure,open craniotomies, endonasal/skull-based/ENT, spine procedures, andother parts of the body such as breast biopsies, liver biopsies, etc.While several examples have been provided, aspects of the presentdisclosure may be applied to any suitable medical procedure.

While one example of a navigation system 205 is provided that may beused with aspects of the present application, any suitable navigationsystem may be used, such as a navigation system using optical trackinginstead of infrared cameras.

Referring to FIG. 4A, a flow chart is shown illustrating a method 400 ofperforming a port-based surgical procedure using a navigation system,such as the medical navigation system 205 described in relation to FIG.2. At a first block 402, the port-based surgical plan is imported. Adetailed description of the process to create and select a surgical planis outlined in international publication WO/2014/139024, entitled“PLANNING, NAVIGATION AND SIMULATION SYSTEMS AND METHODS FOR MINIMALLYINVASIVE THERAPY”, which claims priority to U.S. Provisional PatentApplication Ser. Nos. 61/800,155 and 61/924,993, which are all herebyincorporated by reference in their entirety.

Once the plan has been imported into the navigation system at the block402, the patient is placed on a surgical bed. The head position isconfirmed with the patient plan in the navigation system (block 404),which in one example may be implemented by a computer or controllerforming part of the equipment tower.

Next, registration of the patient is initiated (block 406). The phrase“registration” or “image registration” refers to the process oftransforming different sets of data into one coordinate system. Data mayinclude multiple photographs, data from different sensors, times,depths, or viewpoints. The process of “registration” may beused formedical imaging in which images from different imaging modalities areco-registered. In some instances registration may also be used in orderto be able to compare, map, or integrate the data obtained from thesedifferent modalities with a position of a patient in physical space.

Those skilled in the relevant arts will appreciate that there arenumerous registration techniques available and one or more of thetechniques may be applied to the present example. Non-limiting examplesinclude intensity-based methods that compare intensity patterns inimages via correlation metrics, while feature-based methods findcorrespondence between image features such as points, lines, andcontours. Image registration methods may also be classified according tothe transformation models they use to relate the target image space tothe reference image space. Another classification can be made betweensingle-modality and multi-modality methods. Single-modality methodstypically register images in the same modality acquired by the samescanner or sensor type, for example, a series of magnetic resonance (MR)images may be co-registered, while multi-modality registration methodsare used to register images acquired by different scanner or sensortypes, for example in magnetic resonance imaging (MRI) and positronemission tomography (PET). In the present disclosure, multi-modalityregistration methods may be used in medical imaging of the head and/orbrain as images of a subject are frequently obtained from differentscanners. Examples include registration of brain computerized tomography(CT)/MRI images or PET/CT images for tumor localization, registration ofcontrast-enhanced CT images against non-contrast-enhanced CT images, andregistration of ultrasound and CT to patient in physical space.

Referring now to FIG. 4B, a flow chart is shown illustrating two methodswhich may occur as per registration block 406, outlined in FIG. 4A, ingreater detail. If the use of fiducial touch points (440) iscontemplated, the method involves first identifying fiducials on images(block 442), then touching the touch points with a tracked instrument(block 444). Next, the navigation system computes the patientregistration to reference markers (block 446).

Upon completion of either the fiducial touch points (440) or surfacescan (450) procedures, the data extracted is computed and used toconfirm registration at block 408, shown in FIG. 4A.

Referring back to FIG. 4A, once registration is confirmed (block 408),the patient is draped (block 410). Typically, draping involves coveringthe patient and surrounding areas with a sterile barrier to create andmaintain a sterile field during the surgical procedure. The purpose ofdraping is to eliminate the passage of microorganisms (e.g., bacteria)between non-sterile and sterile areas. At this point, conventionalnavigation systems require that the non-sterile patient reference isreplaced with a sterile patient reference of identical geometry locationand orientation. Numerous mechanical methods may be used to minimize thedisplacement of the new sterile patient reference relative to thenon-sterile one that was used for registration but it is inevitable thatsome error will exist. This error directly translates into registrationerror between the surgical field and pre-surgical images. In fact,generally the further away points of interest are from the patientreference, the worse the error will be.

Upon completion of draping (block 410), the patient engagement pointsare confirmed (block 412) and then the craniotomy is prepared andplanned (block 414).

Upon completion of the preparation and planning of the craniotomy (block414), the craniotomy is cut and a bone flap is temporarily removed fromthe skull to access the brain (block 416). Registration data is updatedwith the navigation system at this point (block 422).

Next, the engagement within craniotomy and the motion range areconfirmed (block 418). Next, the procedure advances to cutting the duraat the engagement points and identifying the sulcus (block 420).

Thereafter, the cannulation process is initiated (block 424).Cannulation involves inserting a port into the brain, typically along asulci path as identified at 420, along a trajectory plan. Cannulation istypically an iterative process that involves repeating the steps ofaligning the port on engagement and setting the planned trajectory(block 432) and then cannulating to the target depth (block 434) untilthe complete trajectory plan is executed (block 424).

Once cannulation is complete, the surgeon then performs resection (block426) to remove part of the brain and/or tumor of interest. The surgeonthen decannulates (block 428) by removing the port and any trackinginstruments from the brain. Finally, the surgeon closes the dura andcompletes the craniotomy (block 430). Some aspects of FIG. 4A arespecific to port-based surgery, such as portions of blocks 428, 420, and434, but the appropriate portions of these blocks may be skipped orsuitably modified when performing non-port based surgery.

Referring now to FIG. 5, a registration process, similar to that whichmay be used in block 456 of FIG. 4B, is shown for computing a transformthat may be used to import coordinates from the physical coordinatespace of the operating room to the image space of the MRI image.Resultantly any tool positions in the physical coordinate space may beregistered to the image space via the application of this transform.

In order to derive this transform for importing objects from a physicalcoordinate space to an image space, the two spaces must be coupled witha “common reference”, having a defined position that can be located inboth the physical and image coordinate spaces. The process of patientregistration for surgical navigation uses identifiable points located ona patient anatomy visible both on the patient and on the patients scanas the common reference point(s). An example of a common reference isshown in FIG. 5 as 500 along with the physical and image coordinatespace origins, 510 and 520 respectively. It is apparent from the figurethat the common references position is known in both spaces. Using thesepositions a transform may be derived that facilitates the importation ofthe position of any point in the physical coordinate space into theimage space. One way to determine the transform is by equating thelocations of the common reference in both spaces and solving for anunknown translation variable for each degree of freedom defined in thetwo coordinate spaces. These translation variables may then be used toconvert a set of coordinates from one space to the other. An exemplarytransform may be derived as per the diagram shown in FIG. 5. In thefigure the position of the common reference 500 is known relative to thephysical coordinate space origin 510 and the image space origin 520. Thecommon references position can be extracted from the diagram as follows:(Xcra,Ycra)=(55,55)and(Xcrv,Ycrv)=(−45,−25)

Where the subscript “cra” denotes the common reference position relativeto the physical coordinate space origin and the subscript “cry” denotesthe common reference position relative to the image space origin.Utilizing a generic translation equation describing any points ((Ya, Xa)and (Yv, Xv)), where the subscript “a” denotes the coordinates of apoint relative to the physical coordinate space origin 510, and thesubscript “v” denotes the coordinates of a point relative to the imagespace origin 520, we can equate the individual coordinate elements fromeach space to solve for translation variables ((YT, XT)), where thesubscript “T” denotes the translation variable as shown below.Yv=Ya+YTXv=Xa+XT

Now substituting the derived values of the points from FIG. 5 we cansolve for the translation variable.−45=55+YT100YTAnd25=55+XT80=XTUtilizing these translation variables, any position ((i.e. (Ya, Xa))defined relative to the common reference in the physical coordinatespace may be transformed into an equivalent position defined relative tothe common reference in the image space through the two generictransformation equations provided below. It should be noted that theseequations may be rearranged to transform any coordinates of a positionfrom the image space into equivalent coordinates of a position in thephysical coordinate space as well.Xa=Xv+100andYa=Yv+80

The calculated transform thus enables the position of any object to betransformed from the physical coordinate space to the image space. Thusthe two spaces become coupled with the transform enabling theregistration of objects from the physical space to the image space. Itshould be noted that in practice the common reference is usually a setof points (as opposed to a single point) from the patients anatomy thatmay be located both on the anatomy of the patient in the physicalcoordinate space of the operating room and in the image of the patient.Using a set of points may be more advantages as it further restrictsdegrees of freedom. More specifically in a spatial coordinate systemsuch as the physical coordinate space of the operating room an objectmay have six degrees of freedom, three spatial degrees of freedom mostcommonly referred to as (x, y, z) and three rotational degrees mostcommonly referred to as (pitch, yaw, roll). Accordingly one manner toduplicate these values upon transformation from the physical coordinatespace to the image space is to transform three or more points from theobject.

To further elaborate on the process of registration two practicalimplementations will be described in further detail as follows. A flowchart describing the two practical methods of performing a patientregistration are provided in FIG. 6. The first method 621 is thetouch-point registration method and the second method 601 is the morerecently established surface trace method. FIG. 7 shows an illustrativediagram of each step in performing a registration using the touch-pointmethod 621. These methods may be employed through the use of thenavigation system and any steps may be programmed into the processor andstored in memory and called upon when needed.

The first step in this method 620 is to initiate the touch-pointacquisition process. During this step a user may prompt the navigationsystem processor such as processor 302 in FIG. 3 to initiate atouch-point acquisition process. To clarify a touchpoint acquisition mayrefer to the priming of the system to acquire a pointer position upondetermining it to be at the position of a fiducial point. In analternate embodiment the system itself may initiate a touch-pointregistration process without the input of the user, such as upon thesystem advancing to the touch-point registration mode, or upon detectionof trackable medical instruments such as by tracking system 321.

Once the touch-point registration process is initiated 620 the followingstep is to acquire one or more fiducial positions 625 in the physicalcoordinate space of the operating room. FIG. 7 depicts an illustrationof this step 625. As is shown in the figure a user 704 is identifyingfiducials 708 on a patient 707 using a tracked pointer tool 702. Thetracking camera 750, connected to the surgical navigation system (notshown), collects the positions of the fiducial points 708 via thetracked pointer tool 702 and passes them to the navigation systemprocessor which either stores the points in the image space containingthe patient image, such as the points 708 in the image space 725, oralternatively in memory, or the like. In some cases the tracking systemis constantly tracking the pointer tool's position. Thus in order torecord the position of the pointer tool at the correct time (i.e. whenit is placed on a fiducial), the system may be prompted by the user.This prompt may be facilitated through the use of a switch type devicesuch as a foot pedal or mouse that is connected to the surgicalnavigation system and are read by the processor for activation. Inaddition an alternate way of prompting the navigation system to recordthe position of the pointer tool when placed on the fiducial may bethrough the use of a gesture. One gesture that may be used to capturethe position of the pointer tool at the correct time may be staticallyholding the pointer tool in the same position for a predetermined amountof time. One benefit of using this gesture based switch over the manualones is that it requires no additional hardware and may be implementedusing the navigation system with the hardware as is.

Once the fiducial points are acquired 625 the following step is toextract the scanned fiducial points from the patient image 630. FIG. 7depicts an illustration of this step. As is shown in the figure thescanned fiducials 710 are segregated from the rest of the patient image706 in the image space 730. In some cases the segregation of thefiducials from the image of the patient may be completed manually by auser, where the user indicates the fiducial positions on the patientimage to the surgical navigation system through a graphical userinterface, such as 372 in FIG. 3. While in other cases the surgicalnavigation system may be programmed with instructions to segregate thepositions of the scanned fiducials from the patient image automatically.Thus step 630 may be performed by either a user or a surgical navigationsystem.

Once the scanned fiducial points are extracted from the patient image630, the following step 635 is to compute a patient registrationtransform. FIG. 7 depicts an illustration of a computed transform 712 asper the example provided. It is apparent from the figure that thetransform 712 is computed such that the fiducial points 708 acquiredfrom the physical coordinate space align with the extracted fiducials710. In general the completion of this step 635 requires the navigationsystem processor to compute a single transform that when applied to eachfiducial point 708 in the image space individually, will align them withtheir scanned fiducial counterparts 710. However given practicallimitations of technology perfect alignment is problematic to achievefor all of the fiducial points using a single transform. Thus toapproximate a perfect alignment the processor instead computes atransform that minimizes the deviation in alignment between theextracted fiducials from the patient image and the fiducial points onthe patient. For example as shown in FIG. 8 the transforms 802 and 804both attempt to align the fiducial points 708 with their counterparts710 in the image space 800. Such transforms may be derived byiteratively applying a cost minimization function to the initial set offiducial points with arguments being the sum of spatial deviances Δx_(a)_(→) _(g) and Δz_(a) _(→) _(g) between the two sets of points 708 and710 For example as shown in FIG. 8 the iterative computation may in oneiteration produce the transform 804 that when applied to the fiducialpoints 708 produces the alignment of points shown in frame 814 of FIG.8. While in a subsequent iteration may produce the transform 802 thatwhen applied to the fiducial points 708 produces the alignment of pointsshown in frame 812 of FIG. 8. The processor may then execute the costminimization function to compare the sum of the deviances Δx_(a) _(→)_(g) and Δz_(a) _(→) _(g) for each result 814 and 812 and select the onewith the lowest value for the next iteration and so on until thedeviation value falls below a certain threshold value or meets somealternately defined criteria. It is apparent from the case shown in FIG.8 that the transform which minimizes the spatial deviances Δx_(a) _(→)_(g) and Δz_(a) _(→) _(g) when applied to the fiducial points 708 is thetransform 812. It should be noted that in the example provided in thefigure the deviances are shown in two dimensions however this should notbe taken to limit the number of dimensions over which these iterativecost minimization functions may be applied.

Referring back to FIG. 6, once step 635 is completed and a patienttransform is derived it may then be used to transform any points fromthe physical coordinate space of the operating room into the imagespace, effectively coupling the two spaces. Referring back to FIG. 7this aspect of the patient registration process is illustrated by thephysical coordinate space 720 and the image space 735 where the spatialalignments between the patient 707, the patient reference 760, and thepointer tool 702 is duplicated by the virtual representations of theseobjects in the image space 735. i.e. by the patient scan 706, thevirtual patient reference 762 and the virtual pointer tool 714 in theimage space 735.

Returning to the flow charts in FIG. 6, the second flow chart 601describes the process of a surface trace patient registration. FIG. 9shows an illustrative diagram of each step in performing a patientregistration using the surface trace method 601. This method may beimplemented using a surgical navigation system as is known in the artand any steps may be programmed into the processor and stored in memoryand recalled as needed.

The first step in this method 600 is to initialize the surface tracepatient registration process. During this step a user may prompt thenavigation system processor such as processor 302 in FIG. 3 to primeitself to receive one or more surface traces of the patient. To clarifya surface trace generally refers to a set of point positions acquiredsequentially, that are identified by guiding a tracked tool over thecontours of a patients surface features by the tracking system. However,it should be noted that alternatively to point positions any data typeable to represent the contours of the patient may be used instead, suchas vectors, curves and the like. In some instances as opposed to theuser initializing the surface trace patient registration process analternate embodiment would be the system itself may initiate a surfacetrace registration process without the input of the user, such as uponthe system advancing to the patient registration step, or upon detectionof trackable medical instruments in the operating room (for example, viatracking system 321), or upon other indicative actions. One such actioncould be detected by the navigation system, such as for example thenavigation system determining that the pointer tool tip has dwelled atthe same position for a predetermined period of time. To elaboratefurther, a user may allow the pointer to dwell in a position until thenavigation system processor recognizes this action and begins seriallyacquiring positions of the pointer at which time the surgical navigationsystem may notify the user that it has begun capturing positions for thesurface trace by producing an audible signal, or in an alternateembodiment may notify the user through the display and GUI of thenavigation system. In some instances the surgical navigation system mayindicate to the user that a trace has begun or ended using an audiblesignal such as a click or a continuous tone until the trace ends. Thetermination of a trace (i.e. the point at which the navigation systemstops serially acquiring positions of the pointer tool) may in someinstances be prompted by many of the trace inducers described above.Furthermore, in addition to dwelling, another gesture that may be usedto terminate the trace would be a fast movement of the pointer. Thisembodiment could be implemented by comparing each new position of thepointer in the series with the previous position of the pointer andchecking to see if that value falls within some tolerance value. In yetanother embodiment the trace may terminate if the pointer tool positionbecomes undetectable. For, example if the pointer tool leaves the fieldof view of the camera.

Once the surface trace registration process is initiated 600 thefollowing step 605 is to acquire one or more surface traces in thephysical coordinate space of the operating room. FIG. 9 depicts anillustration of this step 605. As is shown in the figure a user 704 isguiding a tracked pointer tool 702 along the contours of a patient'sface 707 to acquire the two surface traces 908. The tracking camera 750,connected to the surgical navigation system, collects the positions ofthe surface trace points 908 via the tracked pointer tool 702 and passesthem to the navigation system processor which stores the points in theimage space containing the patient image, such as the points 908 in theimage space 905, in the processor memory, or alternatively any knowncoordinate space. In some instances the tracking system may becontinuously tracks the pointer tool's position in order to record thepositions of the pointer tool during the surface trace (i.e. when it isguided across the features of the patient). During this instance thesystem may be prompted by the user to begin or end the trace. Thisprompt may be facilitated through the use of a switch type device suchas a foot pedal or mouse that are connected to the surgical navigationsystem or in alternate embodiments may be determined by the inertialstate of the pointer tool as determined by the tracking system componentof the surgical navigation system. It should be noted that further waysof beginning or ending the trace are described above in more detail.

Once the surface traces are acquired 605 the following step 610 is toextract the surface from the patient image. FIG. 9 depicts anillustration of this step 610. As is shown in the figure the image ofthe surface 906 of the patient is extracted from the patient image 706in the image space 910. In some cases the extraction of the surface fromthe image of the patient may be completed by the combination of a userand a processor through a GUI. While in other cases the surgicalnavigation system may be programmed with instructions to extract thesurface of the patient from the patient image from automatically. In yetalternate cases the surface may be provided in a useable form (i.e. tocompute a surface trace patient registration via surface matching) bythe 3D imager which acquired the image. Thus step 610 may be performedby either a user or an automated system such as a surgical navigationsystem processor.

Once the surface of the patient is extracted from the patient image 610the following step is to compute a patient registration transform 635.FIG. 9 depicts an illustration of a computed transform 910 as per theexample provided. It is apparent from the figure that the transform 910is computed such that the surface traces 908 acquired from the physicalcoordinate space align with the extracted surface contours 906 (and alsoconsequently the patient image). In general the completion of this step635 requires the navigation system processor to compute a singletransform that when applied to each surface trace 908 in the image spaceindividually, will align them with the extracted surface 906 of thepatient image 706. However given practical limitations of technologyperfect alignment is problematic to achieve for all of the points (orequivalents) in one or more surface traces using a single transform.Thus to approximate a perfect alignment the processor may instead derivea transform that minimizes the deviation in alignment between thesurface 906 extracted from the patient image 706 and the surface traces908 acquired from the patient. However, given the practical limitationsof perfect alignments other algorithmic variants may be usedalternatively to the minimization described above. For example,weighting certain traces and areas of the extracted surface for greaterimportance may be used to provide better overall results. To elaboratefurther upon weighting the traces the cost minimization function may notdepend on a purely one to one alignment error. For example, if weightingis added to some traces or some single points or some areas on thesurface of the patient, then the application of a computed transform mayresult in some regions being better aligned to the traces than the rest.

For example FIG. 10 shows an exemplary diagram depicting the computationof a transform from a surface trace 1008 to an extracted surfacecontour. As is apparent from the figure a patient image 1000 isprocessed to extract its surface 1005. Two contours 1020 and 1025 of theextracted surface 1005 are also provided for illustrative purposes. Thefigure also contains a single surface trace 1008 acquired from thepatient that was scanned such as the patient 707 shown in the previousfigure. Two transformations 1030 and 1035 are shown and applied to thesurface trace 1008. Such transforms may be computed by iterativelyapplying a cost minimization function to the initial surface trace witharguments being the sum of spatial deviance 1028 between the surfacetraces 1008 and the extracted surface of the patient 1005. In oneexample, the iterative cost minimization function may take the form ofan Iterative Closest Point (ICP) approach to calculate the registrationtransformation, such as that detailed in “A Method for Registration of3-D Shapes” by Paul J. Besl and Neil D. McKay, IEEE Transactions onPattern Analysis and Machine Intelligence, pp. 239-256, VOL. 14, No. 2,February 1992, the entirety of which is hereby incorporated byreference. However, any suitable approach may be used depending on thedesign criteria of a particular application. Continuing with thedescription of computing the transform via minimizing the spatialdeviances 1028 as shown in FIG. 10 the iterative computation may in oneiteration produce the transform 1030 that when applied to the surfacetrace 1008 produces the alignment shown in frame 1040 of FIG. 10. Whilein a subsequent iteration may produce the transform 1035 that whenapplied to the surface trace 1008 produces the alignment shown in frame1045 of FIG. 10. The processor may then execute the cost minimizationfunction to compare the sum of the deviances for each result 1030 and1035 and select the one with the lowest value for the next iteration andso on, until the deviation value falls below a certain threshold valueor meets some alternately defined criteria. It should be noted that theterm spatial deviances as used herein may refer to Euclidean distancesbetween the two sets of points for which the deviance is beingcalculated.

Referring back to FIG. 6, once step 635 is completed and a transform 910is derived it may then be used to transform any points from the physicalcoordinate space of the operating room into the image space; effectivelycoupling the two spaces. Referring back to FIG. 9 this aspect of thepatient registration process is illustrated by the physical coordinatespace 900 and the image space 915 where the spatial alignments betweenthe patient 707, the patient reference 760, and the pointer tool 702 isduplicated by the virtual representations of these objects in the imagespace 915. i.e. by the patient scan 706, the virtual patient reference762 and the virtual pointer tool 714. It should be noted that eventhough the surface contours 906 were extracted in the image space, insome instances they may be removed or made invisible if desired. Thismay help reduce visible occlusions of the patient image when a surgeonis operating by using the GUI of the navigation system.

One aspect of the present application provides for methods to improvethe effectiveness of the computed patient transform for a surface tracepatient registration process. Whereby applying the methods may providebetter alignment between points on the patient in the physicalcoordinate space and the extracted surface of the patient image. In someinstances the first of these methods allows the user to modify theacquired surface traces post-acquisition in an attempt to remove anyoutliers or points that cause the alignment to worsen. In some instancesthe second method involves the use of the processor, and through acounting procedure, informs the user of an unbalance in the spatialdistribution of points across the different regions of the patient'sanatomy. In some instances the third method involves the aspect ofweighting the traces so deviances between some surface traces and theextracted surface of the patient may minimized. In some instances thefourth method involves the use of combining registration methodologiesto produce a better result. Accordingly FIG. 11 provides flow chartsdescribing the first three methods. These flow charts describe themethods as an augmentation of the surface trace patient registrationmethod outlined in FIG. 6. More specifically these methods incorporatenew steps in the surface trace patient registration that may improve theoutcome of the registration.

It should be noted that the additional step 1105 of identifyinglandmarks shown in each of the methods 1100, 1102, and 1104 streamlinesthe computation of the transform in the surface trace patientregistration process by providing an initial estimate of the patienttransform. This is accomplished by identifying at least three points onthe patient and deriving a transform similar to the touch point methoddescribed above. Once completed the outputted registration transformfrom this step may be used as an initial estimate in the first iterationof a computation used to derive a final patient registration transformsuch as previously described. For example, the transform outputted bystep 1105 may be used as an initial estimate in the iterative surfacetrace method described in FIG. 6 or it may be incorporated withalternate methods such as those shown in FIG. 11. It should be notedthat since this process is only used to compute an initial estimate ofthe patient registration transform, unlike the touch point method above,the identification of landmark positions (such as the nasion, temple,and tip of the nose, among others) need not necessarily be so exact.i.e. the identification of landmarks may not require the use offiducials. In addition the corresponding positions of the landmarks onthe patient image may be manually identified by the user orautomatically determined by the processor.

Returning to the flow charts in FIG. 11 the first flow chart 1100describes how the computed transform used for patient registration maybe improved via the culling of surface traces acquired in step 610 ofthe surface trace registration process. The additional loop of cullingthe traces follows the computation step 615 and involves the decisionstep 1110 and the action step 1112. The decision step 1110 requires theuser or in alternate embodiments the processor to determine whether thesum of deviations of the one or more surface traces from the extractedsurface of the patient image is under a threshold value which isacceptable. If the deviations are acceptable then the patientregistration is completed using the computed transform 1125. If thedeviations are not acceptable then the surface traces are culled at step1112, a new transform is computed at step 615 and the loop repeats untila transform which produces an acceptable amount of deviances is found.An example implementation of the culling step 1112 is provided in FIG.12. The left side of the figure shows an extracted surface from apatient image 1005 overlaid with transformed surface traces 1201, 1202,and 1203 before any of the traces have been culled. As is apparent fromthe figure there is a significant amount of deviation between theextracted surface 1005 and the surface traces at areas 1205. Thisdeviation may be caused by many factors such as human error for examplethe pointer tool tip being removed from the surface of the patient atthe end of a surface trace, or practical limitations to do with theimage space having a limited resolution, or the accuracy of the trackingsystem in converting coordinates from the physical coordinate space tothe image space, or any other sources of error that may have affectedthe patient registration. Nonetheless one way to account for some ofthis error, such as the accidental lifting of the tool from the patient,would be to cull the trace over that region. For example given the tailend (dashed segment) of surface trace 1203 was acquired when the pointertool was removed from the surface of the patient then culling thesurface trace at that region, for example as indicated by arrow 1213,may reduce the minimum deviation of the optimal transform computable bythe processor of the navigation system. In addition in areas of the headhaving hair (not visible on patient image) a surface trace may have manyregions of inaccurate points because of the hair occluding the surfacesneeded to acquire and accurate trace. For example, given such was thecase for the tail end of surface trace 1202 then removing (culling) thisarea as indicated by arrow 1212 may also reduce the minimum deviation ofthe optimal transform computable by the processor of the navigationsystem. Referring back to FIG. 12 the right side of the figure shows theresults of the fit of the surface traces after the culling of the twotraces 1212, and 1213 were applied. It is apparent that after theculling the fit of the surface traces is better and more specificallythe deviations in areas 1205 of the right side of the figure are reduced

Returning to the flow charts in FIG. 11 the second flow chart 1102describes how the computed transform used for patient registration maybe improved via increasing the spatial coverage of the surface tracesacquired in step 610 of the surface trace registration process. Theadditional loop of assuring sufficient spatial coverage of the surfacetraces follows the surface trace acquisition step 610 and involves thedecision step 1115 and the action step 1117. The decision step 1115requires the user or in alternate embodiments the processor to determinewhether the spatial distribution of points derived from the surfacetraces are sufficiently distributed over the patient anatomy. If thedeviations are acceptable then the patient registration is completedusing the computed transform 1125. If the deviations are not acceptablethen the processor indicates areas on the patient image 1117 wherefurther surface traces are needed. The process then returns to theacquire surface trace stage 610 and the loop is repeated until thesystem captures enough surface traces to assure sufficient coverage ofthe patient image. FIG. 13 illustrates the concept of distribution ofthe surface traces over the patient anatomy. The left side of the figureshows an extracted surface from a patient image 1005 overlaid withtransformed surface traces 1201, 1202, and 1203 before trace 1202 hasbeen acquired by the processor (hence why it's dashed). As is apparentfrom the figure there is a significant amount of deviation between theextracted surface 1005 and the surface traces at areas 1205. Thisdeviation may be caused by many factors such as described above. Howeverit is also apparent from the figure that the fit of the acquired surfacetraces to the data is more vertically deviated than horizontallydeviated. This is more apparent when observing the nose alignment withthe trace 1201. One reason this deviation may have occurred would be asa result of not sufficiently acquiring points from all of the regions ofthe head of the patient. For example the acquisition of points tend tobe mid to bottom heavy and balanced between the left and right. Thus thefit of the trace tends toward the upper areas of the scan. As indicatedabove, one way to address this shortcoming is to identify to the userthat they have not acquired points that are sufficiently distributed onthe patient's anatomy and prompt them for more distributed traces. Forexample, given the processor may segment the patient image in FIG. 13into regions A, B, C, and D. Then from the left side of FIG. 13 it isapparent that the surface traces 1201 and 1203 cover quadrant regions A,D, and C, of the patient image but not quadrant B, thus in step 1117 thesystem may indicate to the user that they should acquire a surface tracein that region (i.e. area B). After subsequently acquiring a surfacetrace, such as trace 1202, and recalculating the patient registrationtransform it is apparent from the right side of FIG. 13 that thealignment of the patient image with the surface traces is now morevertically balanced. Moreover the identification of regions of thepatient where more traces should be acquired via step 1117 may bedetermined using additional metrics other than just the spatialdistribution mentioned above. For example, traces acquired from regionsof the patient having more pronounced features are generally more usefulin computing a transform compared to their more uniform counterparts, asthey tend to have less redundant geometries than other parts of thepatient surfaces. To illustrate this concept when acquiring a surfacetrace of a patient head, the face in comparison to the left side of thehead tends to have more unique geometries than the right side of thehead in comparison to the left side of the head or the top in comparisonto the back of the head. Thus when determining which regions requiremore coverage to prompt the user for acquisition, the navigation systemprocessor may suggest areas based on the amount of unique features asopposed to simply the distribution of surface traces on the image. Itfollows then that in some instances the anatomical areas of the patientimage may be used to define the regions that are used to determine thespatial distribution of traces over the patient.

Returning to the flow charts in FIG. 11 the third flow chart 1104describes how the computed transform used for patient registration maybe improved via the weighting of surface traces acquired in step 610 ofthe surface trace registration process. It should be noted that the termweighting as mentioned above refers to prorating the values of aparticular surface trace when being used to compute the transform and insome instances this may involve normalizing a set of constantsreflective of the relative ranking of each of the traces relative to oneanother. The additional loop of weighting the surface traces follows thecomputation step 615 and is comprised of the decision step 1120 and theaction step 1122. The decision step 1120 requires the user or inalternate embodiments the processor to determine whether the sum ofdeviations of the one or more surface traces from the extracted surfaceof the patient image is under a threshold value which is acceptable. Ifthe deviations are acceptable then the patient registration is completedand the transform 1125 computed. If the deviations are not acceptablethen the surface traces are reweighted at step 1122, a new transform iscomputed at step 615 and the loop repeats until a transform whichproduces an acceptable amount of deviances is found.

FIG. 14 illustrates the effects of applying a greater weight to asurface trace on the computed transform. The left side of the figureshows an extracted surface from a patient image 1005 overlaid withtransformed surface traces 1201, 1202, and 1203 before any of the tracesare ranked and weighted. As is apparent from the figure there is asignificant amount of deviation between the extracted surface 1005 andthe surface traces at areas 1205. This deviation may be caused by manyfactors such as outlined above. Nonetheless one way to account for someof these errors, such as the accidental lifting of the tool from thepatient would be to reweight the trace in that region based on a rankingindicative of the traces accuracy or other factors. For example, giventhe surface trace 1201 was acquired without any preventable issues,while the acquisition of the other two surface traces did not proceed assmoothly it would make sense to rank the trace 1201 higher than theother two such that the deviance of each of the points from the surfacetrace 1201 would be weighted so each would have a greater value than itsnon-ranked version. For example, given the tail-end of the surface trace1203 was acquired when the pointer tool was removed accidentally fromthe surface of the patient by the user, the surface trace 1202 wasacquired by tracing the pointer tool over the occluding hair of thepatient, and consequently the weighted surface trace 1201 was rankeddouble the other two traces, than each unit of distance that 1201deviates from the extracted surface 1005 would be worth double of eachunit of distance than either of the other two surface traces deviatefrom the extracted surface 1005. Thus referring back to the right sideof FIG. 14 we can see that the weighted surface trace 1201 (shown as adouble line because of the weighting) has more of an impact on thetransform as per its greater influence because of its greater weight. Itis apparent from the figure that the surface trace 1201 also resultantlyinfluences the patient registration transform by orienting the extractedsurface further into the left quadrants A and C as compared to theircounterpart traces on the left side of FIG. 14 with no weighting.

In alternate implementations of the system and methods described hereinthe weighting factors as described above may be applied to individualsegments that make up a trace as opposed to the trace itself. Forexample if a surface trace is made up of a plurality of points than thesystem as described herein may allow the user to weigh individual pointsor groups of points at different ranks, potentially magnifying thecapacity of the user to attain the best patient registration. In anotherimplementation the user may select points or groups of points via thesame process in which a trace may be culled as described above. In someembodiments a slider may be used to indicate the segments of a surfacetrace (points, vector, amongst other constituent structures) to beculled or reweighted and a GUI may enable a user to indicate a weightingfor those sections. In other embodiments the slider may be replaced by aswitch in the form of a knob similar to a dimmer switch, or a text boxallowing for an input such that the user may input an index referring tothe sections to be reweighted or culled and their weights, the GUI mayalso allow the user to visually select or outline segments of the traceto be reweighted or culled using for example a cursor controlled by amouse, and any other embodiments such that the user is able to identifythe segment of the surface trace to be culled or reweighted. It shouldbe noted that in this implementation, choosing a segment of a surfacetrace and subsequently assigning it a weight of 0 would affect theregistration transform in effectively the same way as culling the samesegment in the method described above.

In an additional implementation of the system and methods describedherein the surface traces may be weighted based on an estimation of thequality of its acquisition. For example, referring again to FIG. 12 andprocess 1104 the step of reweighting the trace 1122 in the context oftrace 1203 need not be applied broadly to the entire trace 1203 butinstead could be segmented such that only the deviating portion of thetail-end 1213 would receive a lower weight, while the non-highlightedsegment would retain its original weight.

In yet another implementation the unique weighting of the traces (orconstituent structures) may be based on their effectiveness in computingthe registration based on computational metrics. For example traces thatare acquired from regions of the patient having more pronounced featuresare more useful in computing a transform compared to their more uniformcounterparts as they tend to have less redundant geometries than otherparts of the patient surfaces. To illustrate this concept when acquiringa surface trace of a patient head, the face in comparison to the leftside of the head tends to have more unique geometries than the rightside in comparison to the left side of the head or the top and the backof the head. Having an area with these less redundant features thus hasa lower probability of an inaccurate registration. Moreover anothermetric that may be considered would be the density of points per volumeof traces. For example a trace that has a 100 points covering an area of5 mm² has many redundant points compared to a trace with 50 pointscovering an area of 5 cm². Thus weighting the second trace higher thanthe first will likely lead to the computation of a more accuratetransform. It should be noted that the examples of weighting traces andtheir constituent structures as described above were to exemplify thesystem and methods as described herein and should not be construed tolimit the invention and related concepts as disclosed.

In some instances the methods mentioned above may be implemented by thesurgical navigation system as shown in FIG. 3. More specifically anyinteraction between the user and the system may be performed through theuse of the user interface 372 through a display such as that depicted inFIG. 2 and with medical instruments such as 360. More specifically anyof the steps requiring analysis of the deviance of the surface tracesand the extracted surface from the patient may be displayed to the userto provide information regarding the processes being executed such asfor example, while acquiring surface traces after the initial estimateof the registration transform is calculated via step 1105 in FIG. 11.For example as shown in FIG. 15 a GUI showing two traces 1515 and 1510visible atop an extracted patient surface 1500 may be used to determinewhether the transform provides a sufficient accuracy or requires arefinement using one of the methods described herein. For example thegap between the surface trace 1515 and the extracted surface of thepatient 1500 indicated by 1520 may provide the user with enoughinformation to inform them that a refinement is needed. In addition incertain situations the trace may intersect the surface (not shown) whichis also indicative of an inaccurate transform for the patientregistration. Thus a user interface may be implemented to the benefit ofthe user in providing them real-time feed-back of the alignment of thesurface traces with regards to the extracted surface of the patientduring acquisition of the traces. This feature may help streamline theprocess of patient registration as opposed to completing the patientregistration step and subsequently confirming the alignment, such as inthe step 412 of FIG. 4, only to have to return to the previous step 406of initiating the registration and completing the entire registrationprocess again.

In some instances a method that may be used to improve the patientregistration process involves using the touch-point registration asdescribed above in combination with the surface trace registration asdescribed herein. In this additional method, touch points may be addedinto the computation and may reduce the deviance between the surfacetrace points and the extracted surface of the patient image resulting ina better outcome. While in other embodiments such as during thecomputation of a patient-registration using the touch-point methoddescribed above an embodiment of which is shown as 621 in FIG. 6 thesurface trace may be used to supplement missing touch-points or add moreinformation that could be used to refine the patient registration andprovide a better patient registration transform.

The specific embodiments described above have been shown by way ofexample, and it should be understood that these embodiments may besusceptible to various modifications and alternative forms. It should befurther understood that the claims are not intended to be limited to theparticular forms disclosed, but rather to cover modifications,equivalents, and alternatives falling within the spirit and scope ofthis disclosure.

Some aspects of the present disclosure can be embodied, at least inpart, in software, which, when executed on a computing system,transforms an otherwise generic computing system into aspecialty-purpose computing system that is capable of performing themethods disclosed herein, or variations thereof. That is, the techniquescan be carried out in a computer system or other data processing systemin response to its processor, such as a microprocessor, executingsequences of instructions contained in a memory, such as ROM, volatileRAM, non-volatile memory, cache, magnetic and optical disks, or a remotestorage device. Further, the instructions can be downloaded into acomputing device over a data network in a form of compiled and linkedversion. Alternatively, the logic to perform the processes as discussedabove could be implemented in additional computer and/or machinereadable media, such as discrete hardware components as large-scaleintegrated circuits (LSI's), application-specific integrated circuits(ASIC's), or firmware such as electrically erasable programmableread-only memory (EEPROM's) and field-programmable gate arrays (FPGAs).

A computer readable storage medium can be used to store software anddata which when executed by a data processing system causes the systemto perform various methods. The executable software and data may bestored in various places including for example ROM, volatile RAM,nonvolatile memory and/or cache. Portions of this software and/or datamay be stored in any one of these storage devices. As used herein, thephrases “computer readable material” and “computer readable storagemedium” refers to all computer-readable media, except for a transitorypropagating signal per se

The specific embodiments described above have been shown by way ofexample, and it should be understood that these embodiments may besusceptible to various modifications and alternative forms. It should befurther understood that the claims are not intended to be limited to theparticular forms disclosed, but rather to cover all modifications,equivalents, and alternatives falling within the spirit and scope ofthis disclosure.

We claim:
 1. A method of performing a patient registration using asurgical navigation system, having a processor, in a medical procedure,comprising: initializing a surface trace acquisition; recording one ormore surface traces; terminating the surface trace acquisition;receiving a patient image of a patient anatomy; extracting a surfacefrom the patient image; computing a registration transform for patientregistration between the one or more surface traces and the patientimage extracted surface; segmenting the patient image into a pluralityof regions, each region of the plurality of regions containing ananatomical landmark; determining a spatial distribution of surfacetraces among the plurality of regions; determining whether the spatialdistribution in relation to each region of the plurality of regionsminimizes deviance below a threshold; and if the spatial distribution inrelation to any region of the plurality of regions is determined asexceeding the threshold, providing information relating to such region.2. The method of claim 1, wherein computing a registration transformcomprises minimizing a set of Euclidean distances.
 3. The method ofclaim 1, wherein computing a registration transform comprisesiteratively inputting registration transforms into a cost minimizationfunction.
 4. The method of claim 2, further comprising: initializing afiducial position acquisition; recording the positions of fiducials onthe patient; and receiving the location of fiducials points in thepatient image.
 5. The method of claim 1, further comprising the stepsof: monitoring the position of a pointer tool; analyzing the position todetermine if the pointer tool is motionless; and upon determining thatthe pointer tool is motionless for a predetermined amount of time,prompting the surgical navigation system to initialize or terminate thesurface trace.
 6. The method of claim 1, wherein computing aregistration transform further comprises: receiving input from a userranking the one or more surface traces; computing a weighting for thesurface traces based on the ranking; applying the weighting to thesurface traces; and computing a registration transform that minimizes aset of Euclidean distances between the one or more surface traces andthe surface.
 7. The method of claim 1, wherein computing a registrationtransform further comprises: receiving input from a user of one or moreregions of one or more surface traces to be culled; discarding the oneor more regions from the one or more surface traces; and computing aregistration transform that minimizes a set of Euclidean distancesbetween the one or more surface traces and the surface after the regionshave been discarded.
 8. The method of claim 1, wherein computing aregistration transform further comprises: initializing one or morelandmark acquisitions; recording the positions of one or more landmarkson a patient; receiving the position of one or more landmark points inthe patient image; and computing an initial registration transform thatminimizes a set of Euclidean distances between the one or more landmarksand the one or more landmark points.
 9. The method of claim 8, furthercomprising using the initial registration transform to visualize aninitial alignment of the patient's position with the patient image in animage space.
 10. The method of claim 9, further comprising visualizingthe surface traces in the image space.
 11. A surgical navigation systemused for navigated surgical procedures, comprising; a tracked pointertool for identifying positions on the patient; a tracking system fortracking the pointer tool; a processor programmed with instruction to:initialize a surface trace acquisition; continuously record thepositions of the pointer tool during the surface; trace acquisition;combine the positions recorded during the surface trace acquisition intoa surface trace; terminate the surface trace acquisition; receive apatient image of the patient; extract a surface from the patient image;compute a registration transform between the one or more surface tracesand the surface for patient registration; segment the patient image intoa plurality of regions, each region of the plurality of regionscontaining an anatomical landmark; determine a spatial distribution ofsurface traces among the plurality of regions; determine whether thespatial distribution in relation to each region of the plurality ofregions minimizes deviance below a threshold; and if the spatialdistribution in relation to any region of the plurality of regions isdetermined as exceeding the threshold, provide information relating tosuch region.
 12. The system of claim 11, wherein the instruction tocompute a registration transform comprises an instruction to minimize aset of Euclidean distances.
 13. The system of claim 11 or 12, whereinthe instruction to compute a registration transform comprises aninstruction to iteratively input registration transforms into a costminimization function.
 14. The system of claim 12 or 13, wherein theprocessor is programmed with further instruction to: initialize afiducial position acquisition; record the position of the pointer toolduring the fiducial position acquisition; and receive the location offiducials points in the patient image.
 15. The system of claim, whereinthe processor is programmed with further instructions to: monitor theposition of the pointer tool with the tracking system by recording thepointer tool positions; analyze the pointer tool positions to determineif the pointer tool is motionless; and upon determining that the pointertool is motionless for a predetermined amount of time, prompt theprocessor to initialize the surface trace acquisition.
 16. The system ofclaim 11, wherein the instruction to compute a registration transformfurther comprises: an instruction to receive input from a user rankingthe one or more surface traces; an instruction to compute a weightingfor the surface traces based on the ranking; an instruction to apply theweighting to the surface traces; and an instruction to compute aregistration transform that minimizes a set of Euclidean distancesbetween the one or more surface traces and the surface.
 17. The systemof claim 11 or 16, wherein the instruction to compute a registrationtransform further comprises: an instruction to receive input from a userof one or more regions of one or more surface traces to be culled; aninstruction to discard the one or more regions from the one or moresurface traces; and an instruction to compute a registration transformthat minimizes a set of Euclidean distances between the one or moresurface traces and the surface after the regions have been discarded.18. The system of claim 16, further comprising a display having a GUIfor receiving input from a user.
 19. The system of claim 11, wherein theinstruction to compute a registration transform further comprises: aninstruction to initialize one or more landmark acquisitions; aninstruction to record the positions of one or more landmarks on apatient; an instruction to receive the position of one or more landmarkpoints in the patient image; and an instruction to compute an initialregistration transform that minimizes a set of Euclidean distancesbetween the one or more landmarks and the one or more landmark points.20. The system of claim 19, further comprising a display and wherein theprocessor is programmed with further instructions touse the initialregistration transform to visualize, on the display, an initialalignment of the patient's position with the patient image in an imagespace.